We propose to improve the efficiency of genetic programming, a method to automatically evolve computer programs. We use graph-based data mining to identify common aspects of highl...
Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited...
Evolutionary methods have been used to repair programs automatically, with promising results. However, the fitness function used to achieve these results was based on a few simpl...
Ethan Fast, Claire Le Goues, Stephanie Forrest, We...
We use affine arithmetic to improve both the performance and the robustness of genetic programming for symbolic regression. During evolution, we use affine arithmetic to analyze e...
Symbolic regression is a popular genetic programming (GP) application. Typically, the fitness function for this task is based on a sum-of-errors, involving the values of the depe...